Data science/ML/AI
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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
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Сітка:📈 Аналітичний огляд Telegram-каналу Data science/ML/AI
Канал Data science/ML/AI (@datascience_bds) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 13 905 підписників, посідаючи 8 911 місце в категорії Технології та додатки та 28 819 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 13 905 підписників.
За останніми даними від 30 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 90, а за останні 24 години на 7, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 7.35%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.05% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 022 переглядів. Протягом першої доби публікація в середньому набирає 285 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як panda, learning, row, api, ethic.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
Завдяки високій частоті оновлень (останні дані отримано 31 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
13 905
Підписники
+724 години
+17 днів
+9030 день
Архів дописів
13 906
microsoft/Data-Science-For-Beginners
Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
Creator: Microsoft
Stars ⭐️: 11.1k
Forked By: 1.9k
GithubRepo: https://github.com/microsoft/Data-Science-For-Beginners
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13 906
Artificial Neural Networks (ANN) with Keras in Python and R
Rating ⭐️: 4.7 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 143,495
Created by: Start-Tech Academy
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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13 906
UDEMY FREE DEEP LEARNING COURSE
Python for Deep Learning: Build Neural Networks in Python
Rating ⭐️: 4.2 out of 5
Students 👨🏫: 44,894
Created by: Meta Brains
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR 2 DAYS
#python #datanalysis #datascience #deeplearing
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13 906
Repost from Free programming books
The Hundred-Page Machine Learning Book
by Andriy Burkov
📄 152 pages
🔗 Book link
#machinelearning #ml
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13 906
A LITTLE GUIDE TO HANDLING MISSING DATA
Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀
How can you handle these missing values, ensuring you dont loose important part of your data🤷♀️
Not a problem😌. Here are important facts you must know😉
✍️Instances with missing values for all features should be eliminated
✍️Features with high absence rate should either be eliminated or filled with values
✍️Missing values can be replaced using Mean Imputation or Regression Imputation
✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances
✍️Regression Imputation might overfit your model
✍️Mean and Regression Imputation can't be applied to Text features with missing values
✍️Text Features with missing values can be eliminated if not needed in data
✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized
13 906
Repost from Cool GitHub repositories
labmlai/annotated_deep_learning_paper_implementatios
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations
Creator: labml.ai
Stars ⭐️: 7.8k
Forked By: 703
GithubRepo: https://github.com/labmlai/annotated_deep_learning_paper_implementations
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13 906
Data Preprocessing: Understanding and Detecting Outliers
Here's a guide to understanding, detecting and handling outliers👀.
I hope you gain the confidence you need to handle them😁
Outlier Detection and Analysis Methods
Link: Click Me 😌
Detecting and Treating Outliers | Treating the odd one out!
Link: Click Me 😌
Python Treatment for Outliers in Data Science
Link: Click Me 😌
Why You Shouldn’t Just Delete Outliers
Link: Click Me😌
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13 906
UDEMY FREE DATA MANIPULATION AND DEEP LEARNING COURSES
1) Data Manipulation in Python: Master Python, Numpy & Pandas
Rating ⭐️: 4.3 out of 5
Students 👨🏫: 80,451
Created by: Meta Brains
🔗 Course link
2) Python for Deep Learning: Build Neural Networks in Python
Rating ⭐️: 4.2 out of 5
Students 👨🏫: 44,128
Created by: Meta Brains
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR 3 DAYS
#python #datanalysis #datascience #deeplearing #numpy #pandas
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13 906
Let's talk about some simple stat terms - mean, median and mode
Mean, median, and mode are three kinds of "averages". There are many "averages" in statistics, but these are, I think, the three most common, and are certainly the three you are most likely to encounter in your pre-statistics courses, if the topic comes up at all.
The "mean" is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers.
The "median" is the "middle" value in the list of numbers. To find the median, your numbers have to be listed in numerical order from smallest to largest, so you may have to rewrite your list before you can find the median.
The "mode" is the value that occurs most often. If no number in the list is repeated, then there is no mode for the list.
Task:
Find the mean, median, mode, and range for the following list of values:
13, 18, 13, 14, 13, 16, 14, 21, 13
Solution:
mean: 15
median: 14
mode: 13
Explanation:
The mean is the usual average, so I'll add and then divide:
(13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) ÷ 9 = 15
The median is the middle value, so first I'll have to rewrite the list in numerical order:
13, 13, 13, 13, 14, 14, 16, 18, 21
There are nine numbers in the list, so the middle one will be the (9 + 1) ÷ 2 = 10 ÷ 2 = 5th number: 14
The mode is the number that is repeated more often than any other, so 13 is the mode.
13 906
Machine learning for dummies
IBMs limited edition
Judith Hurwitz
Daniel Kirsch
https://www.ibm.com/downloads/cas/GB8ZMQZ3
13 906
Awesome Public Datasets for Your Projects
This contains numerous datasets ranging from :
Agriculture
Biology
Climate+Weather
Complex Networks
Computer Networks
Cyber Security
Data Challenges
Earth Science
Economics
Education
Energy
Entertainment
Finance
...
There's alot you can lay your hands on here
Stars⭐️: 48.8K
Fork: 8.7K
Repo: https://github.com/awesomedata/awesome-public-datasets
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13 906
Facts you need to know about GPUs for Deep Learning
Have you heard about GPUs?🤓 What is GPU and why should i care?🤨
Well I know you might be wondering what this has to do with your deep learning projects😉
Graphics Processing Units (GPUs) are specialized processing cores that you can use to speed computational processes.
It was initially designed to process images and visual data. But now, It is used in reducing the efficiency and power needed to run DL projects,
👌It enables the distribution of training processes and can significantly speed machine learning operations.
👌It is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations.
👌Training models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly.
👌It has a good Video RAM,which frees up CPU for other tasks and providing necessary memory bandwidth for huge datasets.
13 906
Structured vs unstructured data
It is useful to distinguish between structured and unstructured data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about semi-structured data, that have some sort of a structure
that may vary greatly.
13 906
Repost from Free programming books
Mining of Massive Datasets
by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
📄 603 pages
#Data_Mining #Datasets
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